scholarly journals Emergent Scale Invariance and Climate Sensitivity

2018 ◽  
Author(s):  
Rypdal Martin ◽  
Hege-Beate Fredriksen ◽  
Eirik Myrvoll-Nilsen ◽  
Sigrunn H. Sørbye ◽  
Kristoffer Rypdal
Keyword(s):  
Climate Sensitivity ◽  
Scale Invariance ◽  
Radiative Forcing ◽  
Temperature Response ◽  
Temperature Record ◽  
Radiative Balance ◽  
Global Surface ◽  
Earth System Models ◽  
Climate Responses ◽  
Instrumental Temperature

Earth's global surface temperature shows variability on an extended range of temporal scales and satisfies an emergent scaling symmetry. Recent studies indicate that scale invariance is not only a feature of the observed temperature fluctuations, but an inherent property of the temperature response to radiative forcing, and a principle that links the fast and slow climate responses. It provides a bridge between the decadal- and centennial-scale fluctuations in the instrumental temperature record, and the millennial-scale equilibration following perturbations in the radiative balance. In particular, the emergent scale invariance makes it possible to infer equilibrium climate sensitivity (ECS) from the observed relation between radiative forcing and global temperature in the instrumental era. This is verified in ensembles of Earth system models (ESMs), where the inferred values of ECS correlate strongly to estimates from idealized model runs. For the range of forcing data explored in this paper, the method gives best estimates of ECS between 2.3 and 3.4 K.

scholarly journals Emergent Scale Invariance and Climate Sensitivity

Climate ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 93 ◽  
Author(s):  
Martin Rypdal ◽  
Hege-Beate Fredriksen ◽  
Eirik Myrvoll-Nilsen ◽  
Kristoffer Rypdal ◽  
Sigrunn Sørbye
Keyword(s):  
Climate Sensitivity ◽  
Scale Invariance ◽  
Radiative Forcing ◽  
Temperature Response ◽  
Temperature Record ◽  
Radiative Balance ◽  
Global Surface ◽  
Earth System Models ◽  
Climate Responses ◽  
Best Estimates

Earth’s global surface temperature shows variability on an extended range of temporal scales and satisfies an emergent scaling symmetry. Recent studies indicate that scale invariance is not only a feature of the observed temperature fluctuations, but an inherent property of the temperature response to radiative forcing, and a principle that links the fast and slow climate responses. It provides a bridge between the decadal- and centennial-scale fluctuations in the instrumental temperature record, and the millennial-scale equilibration following perturbations in the radiative balance. In particular, the emergent scale invariance makes it possible to infer equilibrium climate sensitivity (ECS) from the observed relation between radiative forcing and global temperature in the instrumental era. This is verified in ensembles of Earth system models (ESMs), where the inferred values of ECS correlate strongly to estimates from idealized model runs. For the range of forcing data explored in this paper, the method gives best estimates of ECS between 1.8 and 3.7 K, but statistical uncertainties in the best estimates themselves will provide a wider likely range of the ECS.

scholarly journals Sensitivity of Global Warming to Carbon Emissions: Effects of Heat and Carbon Uptake in a Suite of Earth System Models

2017 ◽  
Vol 30 (23) ◽  
pp. 9343-9363 ◽  
Author(s):  
Richard G. Williams ◽  
Vassil Roussenov ◽  
Philip Goodwin ◽  
Laure Resplandy ◽  
Laurent Bopp
Keyword(s):  
Climate Sensitivity ◽  
Carbon Emissions ◽  
Radiative Forcing ◽  
Carbon Uptake ◽  
Earth System ◽  
Ocean Heat Uptake ◽  
Surface Warming ◽  
System Models ◽  
Heat Uptake ◽  
Earth System Models

Climate projections reveal global-mean surface warming increasing nearly linearly with cumulative carbon emissions. The sensitivity of surface warming to carbon emissions is interpreted in terms of a product of three terms: the dependence of surface warming on radiative forcing, the fractional radiative forcing from CO2, and the dependence of radiative forcing from CO2 on carbon emissions. Mechanistically each term varies, respectively, with climate sensitivity and ocean heat uptake, radiative forcing contributions, and ocean and terrestrial carbon uptake. The sensitivity of surface warming to fossil-fuel carbon emissions is examined using an ensemble of Earth system models, forced either by an annual increase in atmospheric CO2 or by RCPs until year 2100. The sensitivity of surface warming to carbon emissions is controlled by a temporal decrease in the dependence of radiative forcing from CO2 on carbon emissions, which is partly offset by a temporal increase in the dependence of surface warming on radiative forcing. The decrease in the dependence of radiative forcing from CO2 is due to a decline in the ratio of the global ocean carbon undersaturation to carbon emissions, while the increase in the dependence of surface warming is due to a decline in the ratio of ocean heat uptake to radiative forcing. At the present time, there are large intermodel differences in the sensitivity in surface warming to carbon emissions, which are mainly due to uncertainties in the climate sensitivity and ocean heat uptake. These uncertainties undermine the ability to predict how much carbon may be emitted before reaching a warming target.

Uncertainty in remaining carbon budgets increases with less ambitious targets

2020 ◽  
Author(s):  
Endre Falck Mentzoni ◽  
Andreas Johansen ◽  
Andreas Rostrup Martinsen ◽  
Kristoffer Rypdal ◽  
Martin Rypdal
Keyword(s):  
Radiative Forcing ◽  
Carbon Budget ◽  
Climate Models ◽  
Temperature Response ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Carbon Budgets ◽  
Cumulative Emission ◽  
Internal Climate Variability ◽  
Earth System Models

<blockquote> <div dir="ltr"> <div> <p><span lang="en-US">In this work, we present estimates and uncertainties of the remaining carbon budget for a range of different global temperature targets. To model how atmospheric CO2 and methane concentrations depend on emissions, we use impulse response functions estimated from emission-pulse experiments in Earth System Models (ESMs). We use box-model ESM emulators to model the temperature response to radiative forcing and analyze a range of emission scenarios from Integrated Assessment Models. Taking into account uncertainties in the approximately linear relationship between cumulative emission and peak temperature, as well as internal climate variability and uncertainties in the carbon and climate models, we estimate the remaining carbon budgets for varying targets. The results show that the carbon-budget-uncertainties increase significantly with less ambitious targets.</span></p> </div> </div> </blockquote>

scholarly journals Atmosphere Response Time Scales Estimated from AOGCM Experiments

2012 ◽  
Vol 25 (22) ◽  
pp. 7956-7972 ◽  
Author(s):  
D. J. L. Olivié ◽  
G. P. Peters ◽  
D. Saint-Martin
Keyword(s):  
Response Time ◽  
Time Scales ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
General Circulation ◽  
Temperature Response ◽  
Surface Air Temperature ◽  
General Circulation Models ◽  
Slow Mode ◽  
Scale Estimate

Abstract The global-mean surface air temperature response of the climate system to a specific radiative forcing shows characteristic time scales. Identifying these time scales and their corresponding amplitudes (climate sensitivity) allows one to approximate the response to arbitrary radiative forcings. The authors estimate these time scales for a set of atmosphere–ocean general circulation models (AOGCMs) based on relatively short integrations of 100–300 yr for some idealized forcings. Two modes can be clearly distinguished but a large spread in time scales and climate sensitivities exists among the AOGCMs. The analysis herein also shows that different factors influence the mode estimates. The value and uncertainty of the smallest time scale estimate is significantly lower when based on step scenarios than gradual scenarios; the uncertainty on the climate sensitivity of the slow mode can only be reduced significantly by performing longer AOGCM simulations; and scenarios with only a monotonically increasing forcing do not easily permit the climate sensitivity and the response time for the slow mode to be disentangled. Finally, climate sensitivities can be estimated more accurately than response times.

scholarly journals Reconstruction of the Interannual to Millennial Scale Patterns of the Global Surface Temperature

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 147
Author(s):  
Nicola Scafetta
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Empirical Models ◽  
Natural Variability ◽  
Global Temperature ◽  
Temperature Record ◽  
Equilibrium Climate Sensitivity ◽  
Global Surface Temperature ◽  
Semi Empirical ◽  
Global Surface

Climate changes are due to anthropogenic factors, volcano eruptions and the natural variability of the Earth’s system. Herein the natural variability of the global surface temperature is modeled using a set of harmonics spanning from the inter-annual to the millennial scales. The model is supported by the following considerations: (1) power spectrum evaluations show 11 spectral peaks (from the sub-decadal to the multi-decadal scales) above the 99% confidence level of the known temperature uncertainty; (2) spectral coherence analysis between the independent global surface temperature periods 1861–1937 and 1937–2013 highlights at least eight common frequencies between 2- and 20-year periods; (3) paleoclimatic temperature reconstructions during the Holocene present secular to millennial oscillations. The millennial oscillation was responsible for the cooling observed from the Medieval Warm Period (900–1400) to the Little Ice Age (1400–1800) and, on average, could have caused about 50% of the warming observed since 1850. The finding implies an equilibrium climate sensitivity of 1.0–2.3 °C for CO2 doubling likely centered around 1.5 °C. This low sensitivity to radiative forcing agrees with the conclusions of recent studies. Semi-empirical models since 1000 A.D. are developed using 13 identified harmonics (representing the natural variability of the climate system) and a climatic function derived from the Coupled Model Intercomparison Project 5 (CMIP5) model ensemble mean simulation (representing the mean greenhouse gas—GHG, aerosol, and volcano temperature contributions) scaled under the assumption of an equilibrium climate sensitivity of 1.5 °C. The harmonic model is evaluated using temperature data from 1850 to 2013 to test its ability to predict the major temperature patterns observed in the record from 2014 to 2020. In the short, medium, and long time scales the semi-empirical models predict: (1) temperature maxima in 2015–2016 and 2020, which is confirmed by the 2014–2020 global temperature record; (2) a relatively steady global temperature from 2000 to 2030–2040; (3) a 2000–2100 mean projected global warming of about 1 °C. The semi-empirical model reconstructs accurately the historical surface temperature record since 1850 and hindcasts mean surface temperature proxy reconstructions since the medieval period better than the model simulation that is unable to simulate the Medieval Warm Period.

scholarly journals An empirical model of global climate – Part 2: Implications for future temperature

2012 ◽  
Vol 12 (9) ◽  
pp. 23913-23974 ◽  
Author(s):  
N. R. Mascioli ◽  
T. Canty ◽  
R. J. Salawitch
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Global Climate ◽  
Climate Feedback ◽  
Anthropogenic Aerosol ◽  
Aerosol Radiative Forcing ◽  
The Past ◽  
Global Surface ◽  
Aerosol Radiative

Abstract. IPCC (2007) has shown that atmosphere-ocean general circulation models (GCMs) from various research centers simulate the rise in global mean surface temperature over the past century rather well, yet provide divergent estimates of temperature for the upcoming decades. We use an empirical model of global climate based on a multiple linear regression (MLR) analysis of the past global surface temperature anomalies (ΔT) to explore why GCMs might provide divergent estimates of future temperature. Our focus is on the interplay of three factors: net anthropogenic aerosol radiative forcing (NAA RF), climate feedback (water vapor, clouds, surface albedo) in response to greenhouse gas radiative forcing (GHG RF), and ocean heat export (OHE). Our model is predicated on two key assumptions: whatever climate feedback is needed to account for past temperature rise will persist into the future and whatever fraction of anthropogenic RF (GHG RF + NAA RF) is exported to the oceans to match the observed rise in ocean heat content will also persist. Even with these assumptions, modeled future ΔT mimics the behavior of GCMs because the ~110 record of global surface temperature can not distinguish between two possibilities. If anthropogenic aerosols presently exert small cooling on global climate, feedback must be weak and the future rise in global average surface temperature in 2053, the time CO2 is projected to double according to RCP 8.5, could be moderate. If aerosols presently exert large cooling of global climate, feedback must be large and future ΔT when CO2 doubles could be substantial. Reduced uncertainty for climate projection requires observationally based constraints that can narrow the uncertainties that presently exist for net anthropogenic aerosol radiative forcing as well as the totality of feedbacks that occur in response to a GHG RF perturbation. GCMs are often compared by evaluating the equilibrium response to a doubling of CO2, termed climate sensitivity. In our model framework, ΔT at the time CO2 doubles is nearly independent of OHE, because climate feedback must be adjusted to properly simulate observed temperature. Our simulations show that if a small fraction of anthropogenic RF is exported to the ocean, equilibrium climate sensitivity closely represents the modeled ΔT at the time CO2 doubles. Conversely, if this fraction is large, ΔT when CO2 doubles is much less than the equilibrium climate sensitivity (i.e. the model is now far from equilibrium). Similar behavior likely occurs within GCMs. We therefore suggest the dependence of climate sensitivity on OHE be factored into analyses that use this metric to compare and evaluate GCMs.

scholarly journals Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling

2020 ◽  
Vol 11 (2) ◽  
pp. 329-345
Author(s):  
Eirik Myrvoll-Nilsen ◽  
Sigrunn Holbek Sørbye ◽  
Hege-Beate Fredriksen ◽  
Håvard Rue ◽  
Martin Rypdal
Keyword(s):  
Surface Temperature ◽  
Long Range ◽  
Radiative Forcing ◽  
Temperature Response ◽  
R Package ◽  
Temperature Record ◽  
Modeling Framework ◽  
Transient Climate Response ◽  
Intercomparison Project ◽  
Reliable Quantification

Abstract. Reliable quantification of the global mean surface temperature (GMST) response to radiative forcing is essential for assessing the risk of dangerous anthropogenic climate change. We present the statistical foundations for an observation-based approach using a stochastic linear response model that is consistent with the long-range temporal dependence observed in global temperature variability. We have incorporated the model in a latent Gaussian modeling framework, which allows for the use of integrated nested Laplace approximations (INLAs) to perform full Bayesian analysis. As examples of applications, we estimate the GMST response to forcing from historical data and compute temperature trajectories under the Representative Concentration Pathways (RCPs) for future greenhouse gas forcing. For historic runs in the Model Intercomparison Project Phase 5 (CMIP5) ensemble, we estimate response functions and demonstrate that one can infer the transient climate response (TCR) from the instrumental temperature record. We illustrate the effect of long-range dependence by comparing the results with those obtained from one-box and two-box energy balance models. The software developed to perform the given analyses is publicly available as the R package INLA.climate.

scholarly journals Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling

2019 ◽  
Author(s):  
Eirik Myrvoll-Nilsen ◽  
Sigrunn Holbek Sørbye ◽  
Hege-Beate Fredriksen ◽  
Håvard Rue ◽  
Martin Rypdal
Keyword(s):  
Surface Temperature ◽  
Long Range ◽  
Radiative Forcing ◽  
Temperature Response ◽  
R Package ◽  
Balance Model ◽  
Temperature Record ◽  
Modeling Framework ◽  
Transient Climate Response ◽  
Reliable Quantification

Abstract. Reliable quantification of the global mean surface temperature (GMST) response to radiative forcing is essential for assessing the risk of dangerous anthropogenic climate change. We present the statistical foundations for an observation-based approach, using a stochastic linear-response model that is consistent with the long-range temporal dependence observed in global temperature variability. We have incorporated the model in a latent Gaussian modeling framework, which allows for the use of integrated nested Laplace approximations (INLAs) to perform full Bayesian analysis. As examples of applications, we estimate the GMST response to forcing from historical data and compute temperature trajectories under the Representative Concentration Pathways (RCPs) for future greenhouse gas forcing. For historic runs in the Model Intercomparison Project Phase 5 (CMIP5) ensemble, we estimate response functions and demonstrate that one can infer the transient climate response (TCR) from the instrumental temperature record. We illustrate the effect of long-range dependence by comparing the results with those obtained from a 1-box energy balance model. The software developed to perform the given analyses is publicly available as the R-package INLA.climate.

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